Robust Finger-vein ROI Localization Based on the 3σ Criterion Dynamic Threshold Strategy

Visual Feature-Guided Diamond Convolutional Network for Finger Vein Recognition

Finger vein (FV) biometrics have garnered considerable attention due to their inherent non-contact nature and high security, exhibiting tremendous potential in identity authentication and beyond. Nevertheless, challenges pertaining to the scarcity of training data and inconsistent image quality continue to impede the effectiveness of finger vein recognition (FVR) systems. To tackle these challenges, we introduce the visual feature-guided diamond convolutional network (dubbed 'VF-DCN'), a uniquely configured multi-scale and multi-orientation convolutional neural network. The VF-DCN showcases three pivotal innovations: Firstly, it meticulously tunes the convolutional kernels through multi-scale Log-Gabor filters. Secondly, it implements a distinctive diamond-shaped convolutional kernel architecture inspired by human visual perception. This design intelligently allocates more orientational filters to medium scales, which inherently carry richer information. In contrast, at extreme scales, the use of orientational filters is minimized to simulate the natural blurring of objects at extreme focal lengths. Thirdly, the network boasts a deliberate three-layer configuration and fully unsupervised training process, prioritizing simplicity and optimal performance. Extensive experiments are conducted on four FV databases, including MMCBNU_6000, FV_USM, HKPU, and ZSC_FV. The experimental results reveal that VF-DCN achieves remarkable improvement with equal error rates (EERs) of 0.17%, 0.19%, 2.11%, and 0.65%, respectively, and Accuracy Rates (ACC) of 100%, 99.97%, 98.92%, and 99.36%, respectively. These results indicate that, compared with some existing FVR approaches, the proposed VF-DCN not only achieves notable recognition accuracy but also shows fewer number of parameters and lower model complexity. Moreover, VF-DCN exhibits superior robustness across diverse FV databases.

An Improved Multimodal Biometric Identification System Employing Score-Level Fuzzification of Finger Texture and Finger Vein Biometrics

This research work focuses on a Near-Infra-Red (NIR) finger-images-based multimodal biometric system based on Finger Texture and Finger Vein biometrics. The individual results of the biometric characteristics are fused using a fuzzy system, and the final identification result is achieved. Experiments are performed for three different databases, i.e., the Near-Infra-Red Hand Images (NIRHI), Hong Kong Polytechnic University (HKPU) and University of Twente Finger Vein Pattern (UTFVP) databases. First, the Finger Texture biometric employs an efficient texture feature extracting algorithm, i.e., Linear Binary Pattern. Then, the classification is performed using Support Vector Machine, a proven machine learning classification algorithm. Second, the transfer learning of pre-trained convolutional neural networks (CNNs) is performed for the Finger Vein biometric, employing two approaches. The three selected CNNs are AlexNet, VGG16 and VGG19. In Approach 1, before feeding the images for the training of the CNN, the necessary preprocessing of NIR images is performed. In Approach 2, before the pre-processing step, image intensity optimization is also employed to regularize the image intensity. NIRHI outperforms HKPU and UTFVP for both of the modalities of focus, in a unimodal setup as well as in a multimodal one. The proposed multimodal biometric system demonstrates a better overall identification accuracy of 99.62% in comparison with 99.51% and 99.50% reported in the recent state-of-the-art systems.

A Smartphone-Based sEMG Signal Analysis System for Human Action Recognition

In lower-limb rehabilitation, human action recognition (HAR) technology can be introduced to analyze the surface electromyography (sEMG) signal generated by movements, which can provide an objective and accurate evaluation of the patient's action. To balance the long cycle required for rehabilitation and the inconvenient factors brought by wearing sEMG devices, a portable sEMG signal acquisition device was developed that can be used under daily scenarios. Additionally, a mobile application was developed to meet the demand for real-time monitoring and analysis of sEMG signals. This application can monitor data in real time and has functions such as plotting, filtering, storage, and action capture and recognition. To build the dataset required for the recognition model, six lower-limb motions were developed for rehabilitation (kick, toe off, heel off, toe off and heel up, step back and kick, and full gait). The sEMG segment and action label were combined for training a convolutional neural network (CNN) to achieve high-precision recognition performance for human lower-limb actions (with a maximum accuracy of 97.96% and recognition accuracy for all actions reaching over 97%). The results show that the smartphone-based sEMG analysis system proposed in this paper can provide reliable information for the clinical evaluation of lower-limb rehabilitation.

Fault Diagnosis of Wind Turbine Generators Based on Stacking Integration Algorithm and Adaptive Threshold

Fault alarm time lag is one of the difficulties in fault diagnosis of wind turbine generators (WTGs), and the existing methods are insufficient to achieve accurate and rapid fault diagnosis of WTGs, and the operation and maintenance costs of WTGs are too high. To invent a new method for fast and accurate fault diagnosis of WTGs, this study constructs a stacking integration model based on the machine learning algorithms light gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost), and stochastic gradient descent regressor (SGDRegressor) using publicly available datasets from Energias De Portugal (EDP). This model is automatically tuned for hyperparameters during training using Bayesian tuning, and the coefficient of determination (R²) and root mean square error (RMSE) were used to evaluate the model to determine its applicability and accuracy. The fitted residuals of the test set were calculated, the Pauta criterion (3σ) and the temporal sliding window were applied, and a final adaptive threshold method for accurate fault diagnosis and alarming was created. The model validation results show that the adaptive threshold method proposed in this study is better than the fixed threshold for diagnosis, and the alarm times for the GENERATOR fault type, GENERATOR_BEARING fault type, and TRANSFORMER fault type are 1.5 h, 5.8 h, and 3 h earlier, respectively.

Bioinspired CRISPR-Mediated Cascade Reaction Biosensor for Molecular Detection of HIV Using a Glucose Meter

HIV molecular detection plays a significant role in early diagnosis and antiretroviral therapy for HIV patients. CRISPR technology has recently emerged as a powerful tool for highly sensitive and specific nucleic acid based molecular detection when used in combination with isothermal amplification. However, it remains a challenge to improve the compatibility of such a multienzyme reaction system for simple and sensitive molecular detection. Inspired by the multicompartment structures in a living cell, we present a nanoporous membrane-separated (compartmentalized), artificial, cascade reaction system to improve the compatibility of a CRISPR-mediated multienzyme reaction. We further integrated the multienzyme cascade reaction system with a microfluidic platform and glucose biosensing technology to develop a bioinspired, CRISPR-mediated cascade reaction (CRISPR-MCR) biosensor, enabling HIV molecular detection by a simple glucose meter, analogous to diabetes home testing. We applied the bioinspired CRISPR-MCR biosensor to detect HIV DNA and HIV RNA, achieving a detection sensitivity of 43 copies and 200 copies per test, respectively. Further, we successfully validated the bioinspired biosensor by testing clinical plasma samples of HIV, demonstrating its great application potential for point-of-care testing of HIV virus and other pathogens at home or in resource-limited settings.

Association of Early and Supernormal Vascular Aging categories with cardiovascular disease in the Chinese population

Background

Early Vascular Aging and Supernormal Vascular Aging are two extreme phenotypes of vascular aging, and people in the two categories demonstrate distinct clinical characteristics and cardiovascular prognosis. However, the clinical implication of vascular aging categories in the Asian or Chinese population has not been investigated. We aimed to investigate the association between vascular aging categories and cardiovascular events in a Chinese cohort.

Methods

We explored the association of vascular aging categories with incident cardiovascular disease in a community cohort in Shanghai, China, which included 10,375 participants following up for 4.5 years. Vascular age was predicted by a multivariable linear regression model including classical risk factors and brachial-ankle pulse wave velocity. Early and Supernormal vascular aging groups were defined by 10% and 90% percentiles of Δ-age, which was calculated as chronological minus vascular age.

Results

We found that cardiovascular risk significantly increased in Early [hazard ratio (HR), 1.597 (95% CI, 1.043–2.445)] and decreased in Supernormal [HR, 0.729 (95% CI, 0.539–0.986)] vascular aging individuals, comparing with normal vascular aging subjects. The associations were independent of the Framingham risk score. Early vascular aging individuals also showed an elevated risk of total mortality [HR, 2.614 (95% CI, 1.302–5.249)]. Further, the associations of vascular aging categories with cardiovascular risk were much stronger in females than in males. Vascular aging categories with different cutoff levels expressed as percentiles (10th, 20th, and 25th) of Δ-age showed similar associations with cardiovascular risk.

Conclusions

In conclusion, the vascular aging categories could identify people with different levels of cardiovascular risk in the Chinese population, particularly in women.

An ROI Extraction Method of Finger Vein Images Based on Large Receptive Field Gradient Operator for Accurate Localization of Joint Cavity

Region of interest (ROI) extraction is a key step in finger vein recognition preprocessing. The current method takes the finger region in the vein image as the ROI, but this method cannot obtain better recognition accuracy because it only removes the background noise of the image and ignores factors such as the position and shape of the finger. To solve this problem, we limited the ROI to a fixed region between two finger joint cavities, proposed a new large receptive field gradient operator, and designed and implemented a new method for ROI extraction. It uses a large receptive field to search the target, which is similar to human vision, thus solving the problem of difficult ROI localization for images with large gradient areas. Moreover, for external factors such as noise and uneven illumination in the vein image, the interference factors can be eliminated by averaging them to a larger range with a larger size operator to improve the accuracy of the subsequent matching recognition. To verify the effectiveness of the proposed method, we conducted sufficient matching experiments on three public finger vein datasets. On various datasets, our method significantly reduced the identified EER value, with the lowest EER value reaching 0.96%. The experimental results show that the proposed ROI extraction method can effectively eliminate the influence of finger position, finger shape, and other factors on the subsequent recognition performance, accurately locate the finger joint cavity, and effectively improve the recognition performance.

A Finger Vein Feature Extraction Method Incorporating Principal Component Analysis and Locality Preserving Projections

In the field of biometric recognition, finger vein recognition has received widespread attention by virtue of its advantages, such as biopsy, which is not easy to be stolen. However, due to the limitation of acquisition conditions such as noise and illumination, as well as the limitation of computational resources, the discriminative features are not comprehensive enough when performing finger vein image feature extraction. It will lead to such a result that the accuracy of image recognition cannot meet the needs of large numbers of users and high security. Therefore, this paper proposes a novel feature extraction method called principal component local preservation projections (PCLPP). It organically combines principal component analysis (PCA) and locality preserving projections (LPP) and constructs a projection matrix that preserves both the global and local features of the image, which will meet the urgent needs of large numbers of users and high security. In this paper, we apply the Shandong University homologous multi-modal traits (SDUMLA-HMT) finger vein database to evaluate PCLPP and add "Salt and pepper" noise to the dataset to verify the robustness of PCLPP. The experimental results show that the image recognition rate after applying PCLPP is much better than the other two methods, PCA and LPP, for feature extraction.

A Novel ROI Extraction Method Based on the Characteristics of the Original Finger Vein Image

As the second generation of biometric technology, finger vein recognition has become a research hotspot due to its advantages such as high security, and living body recognition. In recent years, the global pandemic has promoted the development of contactless identification. However, the unconstrained finger vein acquisition process will introduce more uneven illumination, finger image deformation, and some other factors that may affect the recognition, so it puts forward higher requirements for the acquisition speed, accuracy and other performance. Considering the universal, obvious, and stable characteristics of the original finger vein imaging, we proposed a new Region Of Interest (ROI) extraction method based on the characteristics of finger vein image, which contains three innovative elements: a horizontal Sobel operator with additional weights; an edge detection method based on finger contour imaging characteristics; a gradient detection operator based on large receptive field. The proposed methods were evaluated and compared with some representative methods by using four different public datasets of finger veins. The experimental results show that, compared with the existing representative methods, our proposed ROI extraction method is 1/10th of the processing time of the threshold-based methods, and it is similar to the time spent for coarse extraction in the mask-based methods. The ROI extraction results show that the proposed method has better robustness for different quality images. Moreover, the results of recognition matching experiments on different datasets indicate that our method achieves the best Equal Error Rate (EER) of 0.67% without the refinement of feature extraction parameters, and all the EERs are significantly lower than those of the representative methods.

A Novel Finger Vein Recognition Method Based on Aggregation of Radon-Like Features

Finger vein (FV) biometrics is one of the most promising individual recognition traits, which has the capabilities of uniqueness, anti-forgery, and bio-assay, etc. However, due to the restricts of imaging environments, the acquired FV images are easily degraded to low-contrast, blur, as well as serious noise disturbance. Therefore, how to extract more efficient and robust features from these low-quality FV images, remains to be addressed. In this paper, a novel feature extraction method of FV images is presented, which combines curvature and radon-like features (RLF). First, an enhanced vein pattern image is obtained by calculating the mean curvature of each pixel in the original FV image. Then, a specific implementation of RLF is developed and performed on the previously obtained vein pattern image, which can effectively aggregate the dispersed spatial information around the vein structures, thus highlight vein patterns and suppress spurious non-boundary responses and noises. Finally, a smoother vein structure image is obtained for subsequent matching and verification. Compared with the existing curvature-based recognition methods, the proposed method can not only preserve the inherent vein patterns, but also eliminate most of the pseudo vein information, so as to restore more smoothing and genuine vein structure information. In order to assess the performance of our proposed RLF-based method, we conducted comprehensive experiments on three public FV databases and a self-built FV database (which contains 37,080 samples that derived from 1030 individuals). The experimental results denoted that RLF-based feature extraction method can obtain more complete and continuous vein patterns, as well as better recognition accuracy.

Enhanced Multimodal Biometric Recognition Based upon Intrinsic Hand Biometrics

In the proposed study, we examined a multimodal biometric system having the utmost capability against spoof attacks. An enhanced anti-spoof capability is successfully demonstrated by choosing hand-related intrinsic modalities. In the proposed system, pulse response, hand geometry, and finger–vein biometrics are the three modalities of focus. The three modalities are combined using a fuzzy rule-based system that provides an accuracy of 92% on near-infrared (NIR) images. Besides that, we propose a new NIR hand images dataset containing a total of 111,000 images. In this research, hand geometry is treated as an intrinsic biometric modality by employing near-infrared imaging for human hands to locate the interphalangeal joints of human fingers. The L2 norm is calculated using the centroid of four pixel clusters obtained from the finger joint locations. This method produced an accuracy of 86% on the new NIR image dataset. We also propose finger–vein biometric identification using convolutional neural networks (CNNs). The CNN provided 90% accuracy on the new NIR image dataset. Moreover, we propose a robust system known as the pulse response biometric against spoof attacks involving fake or artificial human hands. The pulse response system identifies a live human body by applying a specific frequency pulse on the human hand. About 99% of the frequency response samples obtained from the human and non-human subjects were correctly classified by the pulse response biometric. Finally, we propose to combine all three modalities using the fuzzy inference system on the confidence score level, yielding 92% accuracy on the new near-infrared hand images dataset.